Science, Technology, and Current Futurism
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Science, Technology, and Current Futurism
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Robot could load up dishwasher

Robot could load up dishwasher | Science, Technology, and Current Futurism | Scoop.it

Boris "sees" objects with depth sensors on its face and wrists. In 10 seconds it calculates up to a thousand possible ways to grasp a novel object with its five robotic fingers and plans a path of arm movements to reach its target, avoiding obstructions.

"It's not been programmed to pick it up - it's been programmed to learn how to pick it up," explained Professor Wyatt.

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Brainlike Computers, Learning From Experience

Brainlike Computers, Learning From Experience | Science, Technology, and Current Futurism | Scoop.it
The new computing approach is based on the biological nervous system, specifically on how neurons react to stimuli and connect with other neurons to interpret information.
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Self-completing programs - MIT News Office

Self-completing programs - MIT News Office | Science, Technology, and Current Futurism | Scoop.it
A system that automatically fills in the gaps in programmers’ code becomes more powerful.
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excerpt: " The researchers tested the new version of Sketch on several existing applications, including the automated grading system. In cases where the previous version would “time out,” or take so long to reach a solution that it simply gave up, the new version was able to correct students’ code in milliseconds."

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Programming smart molecules: Machine-learning algorithms could make chemical reactions intelligent

Programming smart molecules: Machine-learning algorithms could make chemical reactions intelligent | Science, Technology, and Current Futurism | Scoop.it
Computer scientists at the Harvard School of Engineering and Applied Sciences (SEAS) and the Wyss Institute for Biologically Inspired Engineering at Harvard University have joined forces to put powerful probabilistic reasoning algorithms in the...
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Adams and Napp designed a tool that can take probabilistic representations of unknowns in the world (probabilistic graphical models, in the language of machine learning) and compile them into a set of chemical reactions that estimate quantities that cannot be observed directly. The key insight is that the dynamics of chemical reactions map directly onto the two types of computational steps that computer scientists would normally perform in silico to achieve the same end.

 



Read more at: http://phys.org/news/2013-12-smart-molecules-machine-learning-algorithms-chemical.html#jCp

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